KR20190042203A - Method for diagnosing noise cause of vehicle - Google Patents

Method for diagnosing noise cause of vehicle Download PDF

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KR20190042203A
KR20190042203A KR1020170133858A KR20170133858A KR20190042203A KR 20190042203 A KR20190042203 A KR 20190042203A KR 1020170133858 A KR1020170133858 A KR 1020170133858A KR 20170133858 A KR20170133858 A KR 20170133858A KR 20190042203 A KR20190042203 A KR 20190042203A
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reference data
vehicle
sound source
source signal
noise
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KR102324776B1 (en
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이동철
정인수
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현대자동차주식회사
기아자동차주식회사
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Priority to KR1020170133858A priority Critical patent/KR102324776B1/en
Priority to US15/825,673 priority patent/US20190114849A1/en
Priority to DE102017221701.4A priority patent/DE102017221701B4/en
Publication of KR20190042203A publication Critical patent/KR20190042203A/en
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    • G—PHYSICS
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    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
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    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
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    • G01—MEASURING; TESTING
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    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
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Abstract

According to the present invention, a method for diagnosing a noise cause of a vehicle comprises the following steps of: receiving, by a control unit, a sound source signal through a microphone installed in the vehicle; transmitting, by the control unit, the received sound source signal to an artificial intelligence server and extracting, by the artificial intelligence server, reference data corresponding to the sound source signal by comparing the received sound source signal with stored reference data after the receiving step; and transmitting, by the artificial intelligence server, the extracted reference data to the control unit and outputting, by the control unit, to a diagnostic apparatus, an output signal including information about the noise cause of the vehicle based on the received reference data after the extracting step.

Description

차량의 소음원인 진단방법 {METHOD FOR DIAGNOSING NOISE CAUSE OF VEHICLE}METHOD FOR DIAGNOSING NOISE CAUSE OF VEHICLE [0002]

본 발명은 차량에서 발생하는 소리정보를 인공지능 서버의 데이터와 비교분석함으로써, 쉽게 차량의 소음 원인을 진단하는 방법에 관한 것이다.The present invention relates to a method of easily diagnosing the cause of noise of a vehicle by comparing sound information generated in a vehicle with data of an artificial intelligence server.

일반적으로 차량에 탑재된 엔진, 변속기 등에서 발생하는 소음은 사람이 직접 청음하여 판단하거나, 차량 전체 소음을 계측한 뒤 개별적 분석을 일일이 수행함으로써 문제 부위를 판단하였다.Generally, the noise generated by the engine, the transmission, etc. mounted on the vehicle is judged by the person himself or by performing the individual analysis after measuring the noise of the entire vehicle.

하지만, 이 경우에 차량 문제현상의 원인을 규명하는데 소요되는 비용과 m/h가 증가하고, 전문가만이 차량의 문제현상을 정확하게 규명할 수 있어 일반인들이 차량의 문제를 분별하는데 큰 어려움이 따른다.However, in this case, the cost and m / h required to identify the cause of the vehicle problem are increased, and only the expert can accurately identify the problem phenomenon of the vehicle, so that it is difficult for the ordinary people to discriminate the problem of the vehicle.

상기의 배경기술로서 설명된 사항들은 본 발명의 배경에 대한 이해 증진을 위한 것일 뿐, 이 기술분야에서 통상의 지식을 가진자에게 이미 알려진 종래기술에 해당함을 인정하는 것으로 받아들여져서는 안 될 것이다.It should be understood that the foregoing description of the background art is merely for the purpose of promoting an understanding of the background of the present invention and is not to be construed as an admission that the prior art is known to those skilled in the art.

KRKR 10-2013-006871710-2013-0068717 AA

본 발명은 이러한 문제점을 해결하기 위하여 제안된 것으로, 차량 내에 구비된 마이크로폰으로부터 수신된 음원신호를 인공지능 서버를 통해 분석하여, 차량의 소음원인을 정밀하게 규명하는 차량의 소음원인 진단방법을 제공하는데 그 목적이 있다.The present invention has been proposed in order to solve such problems, and provides a method of diagnosing a cause of noise of a vehicle, which analyzes the sound source signal received from a microphone provided in a vehicle through an artificial intelligence server and accurately identifies the cause of noise of the vehicle It has its purpose.

상기의 목적을 달성하기 위한 본 발명에 따른 차량의 소음원인 진단방법은 제어부가 차량 내에 설치된 마이크로폰을 통해 음원신호를 수신하는 단계; 상기 수신단계 후, 상기 제어부가 수신된 음원신호를 인공지능 서버에 송신하고, 상기 인공지능 서버가 수신된 음원신호를 기저장된 기준데이터맵과 비교분석하여, 상기 기준데이터맵으로부터 상기 음원신호와 대응되는 기준데이터를 추출하는 단계; 상기 추출단계 후, 상기 인공지능 서버가 추출된 기준데이터를 상기 제어부에 송신하고, 상기 제어부가 수신된 기준데이터에 기반하여 차량의 소음원인에 대한 정보를 포함한 출력신호를 진단장치에 출력하는 단계;를 포함할 수 있다.According to another aspect of the present invention, there is provided a method for diagnosing a cause of noise of a vehicle, the method comprising: receiving a sound source signal through a microphone installed in the vehicle; After the receiving step, the control unit transmits the received sound source signal to the artificial intelligence server, and the artificial intelligence server compares and analyzes the received sound source signal with the pre-stored reference data map, Extracting the reference data; Transmitting the reference data extracted by the artificial intelligence server to the control unit after the extracting step and outputting an output signal including information on the cause of noise of the vehicle to the diagnostic apparatus based on the received reference data; . ≪ / RTI >

상기 마이크로폰은 차량 실내 또는 엔진 측에 설치된 것을 특징으로 할 수 있다.The microphone may be installed in the vehicle interior or on the engine side.

상기 추출단계 시, 상기 인공지능 서버는 수신된 음원신호를 이미지데이터로 변환시킨 다음, 변환된 상기 이미지데이터와 기준데이터맵을 비교하여 대응되는 기준데이터를 추출하는 것을 특징으로 할 수 있다.In the extracting step, the artificial intelligence server may convert the received sound source signal into image data, and then compare the converted image data with a reference data map to extract corresponding reference data.

상기 인공지능 서버는 음원신호를 가버필터(Gabor Filter) 및 멜필터(Mel Filter)를 이용하여 이미지데이터로 변환시키는 것을 특징으로 할 수 있다.The artificial intelligence server may convert the sound source signal into image data using a Gabor filter and a Mel filter.

상기 추출단계 시, 상기 인공지능 서버는 수신된 음원신호를 신경망 기법을 이용하여 특정 파라미터로 변환시킨 다음, 변환된 상기 특정 파라미터와 상기 기준데이터맵을 비교하여 대응되는 기준데이터를 추출하는 것을 특징으로 할 수 있다.In the extracting step, the artificial intelligence server converts the received sound source signal into specific parameters using a neural network technique, then compares the converted specific parameter with the reference data map, and extracts corresponding reference data. can do.

상기 신경망 기법은 엔진회전수(RPM) 데이터를 추가로 적용한 DNN(Deep Neural Network) 또는 CNN(Convolution Neural Network) 기법인 것을 특징으로 할 수 있다.The neural network technique may be a DNN (Deep Neural Network) or a CNN (Convolution Neural Network) technique in which engine RPM data is further applied.

상기 추출단계 시, 상기 인공지능 서버는 수신된 단일 음원신호의 전체시간동안의 음원정보를 이용하여 기준데이터를 추출하는 것을 특징으로 할 수 있다.In the extracting step, the artificial intelligence server may extract the reference data using the sound source information for the entire time of the received single sound source signal.

상기 추출단계 시, 상기 인공지능 서버는 수신된 음원신호를 이미지데이터로 변환하고, 변환된 이미지데이터를 신경망 기법을 이용하여 특정 파라미터로 변환시킨 다음, 변환된 특정 파라미터와 상기 기준데이터맵을 비교하여 대응되는 기준데이터를 추출하는 것을 특징으로 할 수 있다.In the extracting step, the artificial intelligence server converts the received sound source signal into image data, converts the converted image data into specific parameters using a neural network technique, and then compares the converted specific parameters with the reference data map And extracting corresponding reference data.

상기 기준데이터는 소음의 원인이 되는 다수의 차량부품 및 상기 차량부품들의 소음연관률 정보를 포함하고, 상기 출력단계 시, 상기 제어부는 수신된 기준데이터에 기반하여 소음연관률이 높은 순서대로 다수의 차량부품들이 나열되도록 상기 진단장치에 출력신호를 출력하는 것을 특징으로 할 수 있다.Wherein the reference data includes a plurality of vehicle components causing noise and noise association ratio information of the vehicle components, and in the output step, the control unit generates a plurality of noise components in descending order of the noise association ratio based on the received reference data, And outputting an output signal to the diagnostic device so that the vehicle parts are listed.

상술한 바와 같은 구조로 이루어진 차량의 소음원인 진단방법에 따르면 차량 문제발생시 소음원인을 규명하는데 소요되는 비용 및 M/H를 저감할 수 있다.According to the method of diagnosing the cause of noise of a vehicle having the above-described structure, it is possible to reduce the cost and M / H required to identify the cause of noise when a vehicle trouble occurs.

또한, 비전문가인 일반인도 손쉽게 차량의 소음원인을 파악할 수 있다.Also, the non-specialist general can easily grasp the cause of the noise of the vehicle.

도 1은 본 발명의 일 실시예에 따른 차량의 소음원인 진단방법을 도시한 순서도,
도 2는 본 발명의 일 실시예에 따른 차량의 소음원인 진단장치를 도시한 블록도,
도 3은 본 발명의 일 실시예에 따른 진단장치의 동작을 도시한 도면이다.1 is a flowchart illustrating a method for diagnosing a cause of noise of a vehicle according to an embodiment of the present invention;
2 is a block diagram illustrating an apparatus for diagnosing the cause of noise of a vehicle according to an embodiment of the present invention.
3 is a diagram illustrating an operation of a diagnostic apparatus according to an embodiment of the present invention.

이하에서는 첨부된 도면을 참조하여 본 발명의 바람직한 실시 예에 따른 차량의 소음원인 진단방법에 대하여 살펴본다.DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, a method for diagnosing a cause of noise of a vehicle according to a preferred embodiment of the present invention will be described with reference to the accompanying drawings.

도 1은 본 발명의 일 실시예에 따른 차량의 소음원인 진단방법을 도시한 순서도, 도 2는 본 발명의 일 실시예에 따른 차량의 소음원인 진단장치를 도시한 블록도이다.FIG. 1 is a flowchart illustrating a method for diagnosing a cause of noise of a vehicle according to an embodiment of the present invention. FIG. 2 is a block diagram illustrating an apparatus for diagnosing the cause of noise of a vehicle according to an embodiment of the present invention.

도 1 내지 도 2를 참조하면, 본 발명의 차량의 소음원인 진단방법은 제어부(100)가 차량 내에 설치된 마이크로폰(110)을 통해 음원신호를 수신하는 단계(S10); 상기 수신단계(S10) 후, 상기 제어부(100)가 수신된 음원신호를 인공지능 서버(120)에 송신하고, 상기 인공지능 서버(120)가 수신된 음원신호를 기저장된 기준데이터맵과 비교분석하여, 상기 기준데이터맵으로부터 상기 음원신호와 대응되는 기준데이터를 추출하는 단계(S20); 상기 추출단계(S20) 후, 상기 인공지능 서버(120)가 추출된 기준데이터를 상기 제어부(100)에 송신하고, 상기 제어부(100)가 수신된 기준데이터에 기반하여 차량의 소음원인에 대한 정보를 포함한 출력신호를 진단장치(130)에 출력하는 단계(S30);를 포함할 수 있다.1 and 2, a method for diagnosing a cause of noise of a vehicle according to the present invention includes: (S10) receiving a sound source signal through a microphone 110 installed in a vehicle; After the reception step S10, the control unit 100 transmits the received sound source signal to the AI server 120, and the AI server 120 compares the received sound source signal with a previously stored reference data map (S20) extracting reference data corresponding to the sound source signal from the reference data map; After the extracting step S20, the artificial intelligence server 120 transmits the extracted reference data to the control unit 100, and the control unit 100 calculates information about the cause of noise of the vehicle based on the received reference data (S30) of outputting an output signal including the output signal to the diagnostic device 130. FIG.

먼저, 차주 또는 정비사가 진단장치(130)를 매개로 차량의 소음원인 진단을 실행하면, 상기 제어부(100)가 상기 수신단계(S10)를 실시한다.First, when the car or mechanic carries out the noise cause diagnosis of the vehicle through the diagnostic device 130, the control unit 100 performs the reception step S10.

상기 마이크로폰(110)은 차량 내에 설치되되, 구체적으로 차량 실내 또는 엔진 측에 설치될 수 있다. 따라서, 제어부(100)는 마이크로폰(110)을 통해 승객이 탑승하는 차량 실내 측에서의 소리, 엔진룸 측에서 발생하는 소리 등을 음원신호로써 전달받을 수 있다.The microphone 110 is installed in the vehicle, specifically, in the vehicle interior or on the engine side. Accordingly, the control unit 100 can receive the sound from the vehicle interior side, the sound generated from the engine room side, etc., on which the passenger is boarded, through the microphone 110 as a sound source signal.

상기 수신단계(S10)를 통해 음원신호를 수집한 제어부(100)는 인공지능 서버(120)에 음원신호를 송신하고, 인공지능 서버(120)는 수신된 음원신호를 기저장된 기준데이터맵과 비교 분석하여 음원신호와 대응되는 기준데이터를 추출한다.The control unit 100 that has collected the sound source signal through the receiving step S10 transmits the sound source signal to the artificial intelligence server 120. The artificial intelligence server 120 compares the received sound source signal with the pre- And extracts reference data corresponding to the sound source signal.

상기 인공지능 서버(120)는 다양한 고장상황에 따른 소음데이터들을 수집한 후, 이를 딥-러닝(Deep Learning) 기반의 빅데이터 유형으로 분류하여 다수의 기준데이터들로 맵핑된 기준데이터맵을 확보한다. 이후, 제어부(100)로부터 음원신호가 수신되었을 때, 해당 음원신호를 기준데이터맵과 비교분석함으로써 음원신호에 따른 소음원인과 유사한 특성을 가지는 기준데이터를 추출해낼 수 있다(S20).The artificial intelligence server 120 collects noise data according to various failure situations and classifies it into a big data type based on Deep Learning to secure a reference data map mapped to a plurality of reference data . Thereafter, when the sound source signal is received from the control unit 100, the sound source signal is compared with the reference data map, and the reference data having characteristics similar to the noise source according to the sound source signal can be extracted (S20).

여기서, 상기 인공지능 서버(120)는 웹서버 형태로 마련되어 차주 또는 정비사가 용이하게 접근하여 소음진단을 실시하도록 마련될 수 있다.Here, the artificial intelligence server 120 is provided in the form of a web server, and can be easily accessed by a borrower or mechanic to diagnose noise.

상기 인공지능 서버(120)가 기준데이터를 추출한 경우, 이를 상기 제어부(100)에 송신하고, 제어부(100)가 수신된 기준데이터에 기반하여 차량의 소음원인 정보를 진단장치(130)에 출력함으로써, 차주 또는 정비사가 진단장치(130)를 통해 차량의 소음원인을 파악할 수 있도록 마련한다.When the artificial intelligence server 120 extracts the reference data, the controller 100 transmits the reference data to the controller 100, and the controller 100 outputs noise cause information of the vehicle to the diagnostic apparatus 130 based on the received reference data , A driver or a mechanic can identify the cause of noise of the vehicle through the diagnostic device 130.

상기 진단장치(130)는 차주 또는 정비사가 차량 소음의 원인을 파악하도록 디스플레이부를 포함하도록 마련되는 것이 바람직하며, 차량의 소음원인에 대한 정보는 디스플레이부로 출력될 수 있다.The diagnostic apparatus 130 may be configured to include a display unit so that the driver or the mechanic can grasp the cause of the vehicle noise, and information on the cause of the noise of the vehicle may be output to the display unit.

좀 더 구체적으로, 상기 추출단계(S20) 시, 상기 인공지능 서버(120)는 수신된 음원신호를 이미지데이터로 변환시킨 다음, 변환된 상기 이미지데이터와 기준데이터맵을 비교하여 대응되는 기준데이터를 추출하도록 마련될 수 있다.More specifically, in the extraction step (S20), the AI server 120 converts the received sound source signal into image data, then compares the converted image data with the reference data map, And extracted.

즉, 인공지능 서버(120)는 소리(Sound) 형태의 음원신호를 시간 또는 주파수를 기준으로 이미지(Image) 형태의 이미지데이터로 변환하고, 변환된 이미지데이터를 대표하는 특징 벡터를 기준데이터맵과 비교함으로써 대응되는 소음형태의 기준데이터를 추출할 수 있다. 이때, 인공지능 서버(120)에 저장된 기준데이터들도 이미지(Image) 형태로 마련되는 것이 바람직할 것이다.That is, the artificial intelligence server 120 converts a sound source signal of a sound type into image data of an image type on the basis of time or frequency, converts a feature vector representing the converted image data into a reference data map It is possible to extract reference data of the corresponding noise type. At this time, the reference data stored in the AI server 120 may be provided in the form of an image.

이와 같이 음원신호를 이미지데이터로 변환하여 대응되는 기준데이터를 추출함으로써, 다양한 소음원이 섞여있는 음원신호로부터 특정 소음을 추출하여 딥러닝 학습을 수행하거나, 해당 소음과 대응되는 기준데이터와 정확하게 비교 분석할 수 있다.By converting the sound source signal into the image data and extracting the corresponding reference data as described above, it is possible to perform a deep learning learning by extracting a specific noise from a sound source signal in which various noise sources are mixed, or accurately compare and analyze the reference data corresponding to the corresponding noise .

이때, 상기 인공지능 서버(120)는 음원신호를 가버필터(Gabor Filter) 및 멜필터(Mel Filter)를 이용하여 이미지데이터로 변환시킬 수 있다.At this time, the artificial intelligence server 120 may convert the sound source signal into image data using a Gabor filter and a Mel filter.

또 다른 방법으로써, 상기 추출단계(S20) 시, 상기 인공지능 서버(120)는 수신된 음원신호를 신경망 기법을 이용하여 특정 파라미터로 변환시킨 다음, 변환된 상기 특정 파라미터와 상기 기준데이터를 비교하여 대응되는 기준데이터를 추출할 수 있다.Alternatively, in the extracting step S20, the AI server 120 converts the received sound source signal into a specific parameter using a neural network technique, and then compares the converted specific parameter with the reference data The corresponding reference data can be extracted.

여기서, 신경망 기법은 엔진회전수(RPM) 데이터를 추가로 적용한 DNN(Deep Neural Network) 또는 CNN(Convolution Neural Network) 기법일 수 있다.Here, the neural network technique may be a DNN (Deep Neural Network) or a CNN (Convolution Neural Network) technique in which engine RPM data is further applied.

상기 DNN 기법과 CNN 기법은 인공지능 머신러닝을 위해 정확도를 향상시키는 신경망 기법으로써, 음원신호를 시간이나 주파수 필터링한 다음, 특정 파라미터 즉, 차량 위치 또는 차량 부품별로 소음종류를 분류하는 기법을 의미한다.The DNN technique and the CNN technique are neural network techniques that improve the accuracy for artificial intelligence machine learning. The DNN technique and the CNN technique are time / frequency-filtered sound source signals and classify noise parameters by specific parameters .

따라서, 인공지능 서버(120)는 변환된 특정 파라미터들과 대응되는 기준데이터들을 추출함으로써, 음원신호로부터 다양한 형태의 소음종류를 구분하고 비교 분석하여 자동차 소음원인의 변별력 및 정확도를 향상시킬 수 있다.Accordingly, the artificial intelligence server 120 can extract the reference data corresponding to the converted specific parameters, discriminate various kinds of noise types from the sound source signals, and compare and analyze them to improve discrimination power and accuracy of the cause of the automobile noise.

또한, 상기 신경망 기법은 자동차 소음원을 구분한다는 특수한 조건을 반영하기 위해 엔진회전수(RPM) 정보를 추가로 적용함으로써, 회전수에 기인한 소음원과 회전수에 기인하지 않는 소음원의 특징을 분류해낼 수 있다. 따라서, 차량의 소음원 분류 정확도를 향상시킬 수 있다.In addition, the neural network technique can further classify the characteristics of the noise sources due to the number of revolutions and the number of revolutions due to the application of the engine revolution number (RPM) information in order to reflect special conditions for classifying the vehicle noise sources have. Therefore, the noise source classification accuracy of the vehicle can be improved.

한편, 상기 추출단계(S20) 시, 상기 인공지능 서버(120)는 수신된 단일 음원신호의 전체시간동안의 음원정보를 이용하여 기준데이터를 추출할 수 있다.Meanwhile, in the extraction step S20, the AI server 120 may extract the reference data using the sound source information for the entire time of the received single sound source signal.

종래의 인공지능 알고리즘은 정형화된 음원신호(30초)를 이용하여 20~40 sec 단위로 학습모델을 생성하고, 이에 대한 결과를 이용한 학습을 수행하였다. 하지만, 이는 차량 소음원과 같은 다양한 음원이 섞여있는 음원신호에서 소음원들을 변별하는 능력을 저하시키는 요인이 되었다.The conventional artificial intelligence algorithm generates a learning model in 20-40 sec units using a formal sound source signal (30 seconds) and performs learning using the result. However, this degrades the ability to distinguish noise sources from source signals mixed with various sources such as vehicle noise sources.

본 기술은 이를 고려하여 하나의 음원신호 전체에 대해 긴 시간 학습을 완료한 후 이를 이용하여 학습을 수행하는 알고리즘을 적용함으로써, 학습모델의 정확도를 향상시켜 정확하게 기준데이터를 추출할 수 있다. 따라서, 짧은 시간에 발생하는 소음원과, 긴 시간에 발생하는 소음원을 모두 학습할 수 있다.In consideration of the above, the present invention can accurately extract the reference data by improving the accuracy of the learning model by applying an algorithm that performs learning using the complete time-series learning of the whole sound source signal. Therefore, both the noise source occurring in a short time and the noise source occurring in a long time can be learned.

또는, 본 발명의 차량의 소음원인 진단방법에 있어서, 상기 추출단계(S20) 시, 상기 인공지능 서버(120)는 수신된 음원신호를 이미지데이터로 변환하고, 변환된 이미지데이터를 신경망 기법을 이용하여 특정 파라미터로 변환시킨 다음, 변환된 특정 파라미터와 상기 기준데이터를 비교하여 대응되는 기준데이터를 추출하도록 마련될 수 있다.Alternatively, in the method for diagnosing a cause of noise of a vehicle of the present invention, in the extracting step (S20), the AI server 120 converts the received sound source signal into image data, and uses the neural network technique To convert the specific parameter into a specific parameter, and then to compare the converted specific parameter with the reference data to extract corresponding reference data.

즉, 인공지능 서버(120)는 제어부(100)로부터 수신된 음원신호를 2단계에 걸쳐서 변환을 실시한 후 기준데이터와 비교분석하기 때문에, 이미지데이터의 특징 벡터에 기반하여 정확하게 소음종류를 구분하는 방식과, 다양한 소음형태를 변별할 수 있는 신경망 기법의 장점을 모두 가질 수 있다. 따라서, 차량의 소음원인을 정확하면서도 변별력있게 진단할 수 있다.That is, since the artificial intelligence server 120 converts the sound source signal received from the control unit 100 into two-stage conversion and then compares and analyzes the sound source signal with the reference data, the artificial intelligence server 120 can correctly distinguish the types of noise based on the feature vectors of the image data And the neural network technique that can distinguish various types of noise. Therefore, the cause of the noise of the vehicle can be diagnosed accurately and discriminately.

여기서, 상기 기준데이터는 소음의 원인이 되는 다수의 차량부품 및 상기 차량부품들의 소음연관률 정보를 포함하고, 상기 출력단계(S30) 시, 상기 제어부(100)는 수신된 기준데이터에 기반하여 소음연관률이 높은 순서대로 다수의 차량부품들이 나열되도록 상기 진단장치(130)에 출력신호를 출력할 수 있다.Here, the reference data includes noise component ratio information of a plurality of vehicle components and the vehicle components that cause noise, and in the output step S30, the control unit 100 determines noise based on the received reference data, An output signal may be output to the diagnostic device 130 such that a plurality of vehicle parts are listed in order of high association ratio.

도 3은 본 발명의 일 실시예에 따른 진단장치(130)의 동작을 도시한 도면이다. 도 3을 참조하면, 제어부는 인공지능 서버로부터 수신된 기준데이터에 기반하여 출력신호를 생성하되, 진단장치(130)의 디스플레이부를 통해 차량의 소음이 특정 차량부품들의 소음과 대응되는지, 또한 해당 차량부품들의 소음연관률이 어느 정도인지 출력되도록 하는 출력신호를 송신한다.3 is a diagram illustrating an operation of the diagnostic apparatus 130 according to an embodiment of the present invention. 3, the control unit generates an output signal based on the reference data received from the artificial intelligence server, and determines whether the noise of the vehicle corresponds to the noise of the specific vehicle parts through the display unit of the diagnostic device 130, And transmits an output signal to output a degree of noise association of the parts.

여기서, 기준데이터는 소음의 원인이 되는 다수의 차량 부품과 그 소음연관률 정보에 더해서, 소음의 원인이 되는 차량 위치나, 그 위치의 소음연관률 정보를 포함하도록 마련될 수도 있다.Here, the reference data may be provided to include, in addition to a plurality of vehicle parts that cause noise, and the noise association ratio information, a vehicle position that causes noise and noise association ratio information of the position.

따라서, 차주 또는 정비사가 진단장치(130)의 디스플레이부를 확인하여 다수의 차량부품들 중 어느 부품이 소음의 원인과 관련되는지를 쉽게 파악할 수 있다.Thus, the driver or mechanic can check the display portion of the diagnostic device 130 to easily identify which of the plurality of vehicle components is related to the cause of the noise.

상술한 바와 같은 구조로 이루어진 차량의 소음원인 진단방법에 따르면 차량 문제발생시 소음원인을 규명하는데 소요되는 비용 및 M/H를 저감할 수 있다.According to the method of diagnosing the cause of noise of a vehicle having the above-described structure, it is possible to reduce the cost and M / H required to identify the cause of noise when a vehicle trouble occurs.

또한, 비전문가인 일반인도 손쉽게 차량의 소음원인을 파악할 수 있다.Also, the non-specialist general can easily grasp the cause of the noise of the vehicle.

본 발명은 특정한 실시예에 관련하여 도시하고 설명하였지만, 이하의 특허청구범위에 의해 제공되는 본 발명의 기술적 사상을 벗어나지 않는 한도 내에서, 본 발명이 다양하게 개량 및 변화될 수 있다는 것은 당 업계에서 통상의 지식을 가진 자에게 있어서 자명할 것이다.While the present invention has been particularly shown and described with reference to specific embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the following claims It will be apparent to those of ordinary skill in the art.

100: 제어부
110: 마이크로폰
120: 인공지능 서버
130: 진단장치100:
110: microphone
120: Artificial Intelligence Server
130: Diagnostic device

Claims (9)

제어부가 차량 내에 설치된 마이크로폰을 통해 음원신호를 수신하는 단계;
상기 수신단계 후, 상기 제어부가 수신된 음원신호를 인공지능 서버에 송신하고, 상기 인공지능 서버가 수신된 음원신호를 기저장된 기준데이터맵과 비교분석하여, 상기 기준데이터맵으로부터 상기 음원신호와 대응되는 기준데이터를 추출하는 단계;
상기 추출단계 후, 상기 인공지능 서버가 추출된 기준데이터를 상기 제어부에 송신하고, 상기 제어부가 수신된 기준데이터에 기반하여 차량의 소음원인에 대한 정보를 포함한 출력신호를 진단장치에 출력하는 단계;를 포함하는 차량의 소음원인 진단방법.The control unit receiving a sound source signal through a microphone installed in the vehicle;
After the receiving step, the control unit transmits the received sound source signal to the artificial intelligence server, and the artificial intelligence server compares and analyzes the received sound source signal with the pre-stored reference data map, Extracting the reference data;
Transmitting the reference data extracted by the artificial intelligence server to the control unit after the extracting step and outputting an output signal including information on the cause of noise of the vehicle to the diagnostic apparatus based on the received reference data; Wherein the vehicle is a vehicle. 청구항 1에 있어서,
상기 마이크로폰은 차량 실내 또는 엔진 측에 설치된 것을 특징으로 하는 차량의 소음원인 진단방법.The method according to claim 1,
Wherein the microphone is installed in a vehicle interior or on an engine side. 청구항 1에 있어서,
상기 추출단계 시, 상기 인공지능 서버는 수신된 음원신호를 이미지데이터로 변환시킨 다음, 변환된 상기 이미지데이터와 기준데이터맵을 비교하여 대응되는 기준데이터를 추출하는 것을 특징으로 하는 차량의 소음원인 진단방법.The method according to claim 1,
Wherein the artificial intelligence server converts the received sound source signal into image data and then compares the converted image data with a reference data map and extracts corresponding reference data. Way. 청구항 3에 있어서,
상기 인공지능 서버는 음원신호를 가버필터(Gabor Filter) 및 멜필터(Mel Filter)를 이용하여 이미지데이터로 변환시키는 것을 특징으로 하는 차량의 소음원인 진단방법.The method of claim 3,
Wherein the artificial intelligence server converts the sound source signal into image data using a Gabor filter and a Mel filter. 청구항 1에 있어서,
상기 추출단계 시, 상기 인공지능 서버는 수신된 음원신호를 신경망 기법을 이용하여 특정 파라미터로 변환시킨 다음, 변환된 상기 특정 파라미터와 상기 기준데이터맵을 비교하여 대응되는 기준데이터를 추출하는 것을 특징으로 하는 차량의 소음원인 진단방법.The method according to claim 1,
In the extracting step, the artificial intelligence server converts the received sound source signal into specific parameters using a neural network technique, then compares the converted specific parameter with the reference data map, and extracts corresponding reference data. A method for diagnosing a cause of noise in a vehicle. 청구항 5에 있어서,
상기 신경망 기법은 엔진회전수(RPM) 데이터를 추가로 적용한 DNN(Deep Neural Network) 또는 CNN(Convolution Neural Network) 기법인 것을 특징으로 하는 차량의 소음원인 진단방법.The method of claim 5,
Wherein the neural network technique is a DNN (Deep Neural Network) or a CNN (Convolution Neural Network) technique in which engine RPM data is further applied. 청구항 5에 있어서,
상기 추출단계 시, 상기 인공지능 서버는 수신된 단일 음원신호의 전체시간동안의 음원정보를 이용하여 기준데이터를 추출하는 것을 특징으로 하는 차량용 소음원인 진단방법.The method of claim 5,
Wherein in the extracting step, the artificial intelligence server extracts reference data using the sound source information for the whole time of the received single sound source signal. 청구항 1에 있어서,
상기 추출단계 시, 상기 인공지능 서버는 수신된 음원신호를 이미지데이터로 변환하고, 변환된 이미지데이터를 신경망 기법을 이용하여 특정 파라미터로 변환시킨 다음, 변환된 특정 파라미터와 상기 기준데이터맵을 비교하여 대응되는 기준데이터를 추출하는 것을 특징으로 하는 차량의 소음원인 진단방법.The method according to claim 1,
In the extracting step, the artificial intelligence server converts the received sound source signal into image data, converts the converted image data into specific parameters using a neural network technique, and then compares the converted specific parameters with the reference data map And extracting the corresponding reference data. 청구항 1, 3, 5 또는 8 중 어느 한 항에 있어서,
상기 기준데이터는 소음의 원인이 되는 다수의 차량부품 및 상기 차량부품들의 소음연관률 정보를 포함하고,
상기 출력단계 시, 상기 제어부는 수신된 기준데이터에 기반하여 소음연관률이 높은 순서대로 다수의 차량부품들이 나열되도록 상기 진단장치에 출력신호를 출력하는 것을 특징으로 하는 차량의 소음원인 진단방법.The method according to claim 1, 3, 5 or 8,
Wherein the reference data includes a plurality of vehicle components causing noise and noise association ratio information of the vehicle components,
Wherein the control unit outputs an output signal to the diagnostic apparatus so that a plurality of vehicle parts are listed in order of the noise association rate based on the received reference data.
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